What it does

This repo holds one developer’s worked solutions to all three main assignments from Stanford’s CS231n Spring 2017 (Convolutional Neural Networks for Visual Recognition). The assignments cover k-NN, SVM, softmax, neural nets, CNNs, RNNs, and GANs. Both PyTorch and TensorFlow versions are included for assignments 2 and 3.

The interesting bit

The author is upfront about why they did it: to get better at Python and deep learning. No false claims of state-of-the-art results, no rewritten course notes — just the actual notebooks, warts and all. That honesty makes it a safer reference than many “perfect” solution sets that hide their mess.

Key highlights

• All three assignments completed (k-NN through GANs)
• Dual implementations: PyTorch and TensorFlow for assignments 2 and 3
• Direct links to the official course materials at cs231n.github.io
• Author responds to issues — rare for a personal coursework repo
• 600+ stars suggest it has served as a sanity-check for other students

Caveats

• Extra credit tasks remain unfinished (explicitly noted by the author)
• This is one person’s solutions, not official or verified correct
• 2017 course version; some APIs and best practices have shifted since

Verdict

Grab this if you’re currently slogging through CS231n and need to compare your approach when stuck. Skip it if you want a standalone tutorial — you’ll need the lectures and notes for context.